Machine Comprehension using Rich Semantic Representations

Machine comprehension tests the system’s ability to understand a piece of text through a reading comprehension task. For this task, we propose an approach using the Abstract Meaning Representation (AMR) formalism. We construct meaning representation graphs for the given text and for each question-answer pair by merging the AMRs of comprising sentences using cross-sentential phenomena such as coreference and rhetorical structures. Then, we reduce machine comprehension to a graph containment problem. We posit that there is a latent mapping of the question-answer meaning representation graph onto the text meaning representation graph that explains the answer. We present a unified max-margin framework that learns to find this mapping (given a corpus of texts and question-answer pairs), and uses what it learns to answer questions on novel texts. We show that this approach leads to state of the art results on the task.